DocumentCode :
779779
Title :
Emergent electricity customer classification
Author :
Chicco, G. ; Napoli, R. ; Piglione, F. ; Postolache, P. ; Scutariu, M. ; Toader, C.
Author_Institution :
Dipt. di Ingegneria Elettrica Ind., Torino, Italy
Volume :
152
Issue :
2
fYear :
2005
fDate :
3/4/2005 12:00:00 AM
Firstpage :
164
Lastpage :
172
Abstract :
Various techniques for electricity customer classification are presented and discussed, with the focus on highlighting the behaviour of electricity customers. The surveyed techniques include classical approaches (applications of statistics and deterministic clustering algorithms), as well as methods based on artificial intelligence (neural networks and fuzzy systems). The classification techniques are illustrated by using various sets of features characterising the shape of the load patterns. Different approaches for feature selection, both in the time and in the frequency domain, are discussed. A number of specific metrics, some of which were originally developed by the authors, are applied in order to quantify the classification adequacy and to identify the most suitable classification techniques. Detailed results obtained from real life applications are provided.
Keywords :
classification; customer profiles; deterministic algorithms; fuzzy systems; neural nets; pattern clustering; power engineering computing; statistical analysis; artificial intelligence; classification statistics; deterministic clustering algorithms; electricity customer behaviour; electricity customer classification; fuzzy systems; neural networks;
fLanguage :
English
Journal_Title :
Generation, Transmission and Distribution, IEE Proceedings-
Publisher :
iet
ISSN :
1350-2360
Type :
jour
DOI :
10.1049/ip-gtd:20041243
Filename :
1421133
Link To Document :
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